Medical Defense Nanorobots (MDNRs): a new evaluation and selection of controller criteria for improved disease diagnosis and patient safety using NARMA(L2)-FOP + D(ANFIS)µ – Iλ-based Archimedes Optimization Algorithm
This article addresses the complexity of optimizing movements in Medical Defense Nanorobots (MDNRs) by proposing a novel integration approach. The challenge lies in selecting the Archimedes Optimization Algorithm (AOA) for MDNR movements, considering specific criteria for fractional-order proportion...
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| Veröffentlicht in: | International journal of information technology (Singapore. Online) Jg. 17; H. 7; S. 3935 - 3945 |
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| Sprache: | Englisch |
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01.09.2025
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| Abstract | This article addresses the complexity of optimizing movements in Medical Defense Nanorobots (MDNRs) by proposing a novel integration approach. The challenge lies in selecting the Archimedes Optimization Algorithm (AOA) for MDNR movements, considering specific criteria for fractional-order proportional-integral-derivative (FOPID) controller gains. To overcome this, the study introduces a three-phase approach: MDNR-based NARMA-L2 controller Pre-process and Identification, Enhancement of NARMA-L2 controller-based NARMA(L2)-
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| AbstractList | This article addresses the complexity of optimizing movements in Medical Defense Nanorobots (MDNRs) by proposing a novel integration approach. The challenge lies in selecting the Archimedes Optimization Algorithm (AOA) for MDNR movements, considering specific criteria for fractional-order proportional-integral-derivative (FOPID) controller gains. To overcome this, the study introduces a three-phase approach: MDNR-based NARMA-L2 controller Pre-process and Identification, Enhancement of NARMA-L2 controller-based NARMA(L2)-FOP+D(ANFIS)μ-Iλ, and Evaluation of FOPID criteria-based AOA. This approach integrates NARMA-L2 for criterion weighting and ANFIS for AOA selection, validated through NARMA(L2)-FOP+D(ANFIS)μ-Iλ evaluation, showcasing the efficacy of the proposed methodology. This article addresses the complexity of optimizing movements in Medical Defense Nanorobots (MDNRs) by proposing a novel integration approach. The challenge lies in selecting the Archimedes Optimization Algorithm (AOA) for MDNR movements, considering specific criteria for fractional-order proportional-integral-derivative (FOPID) controller gains. To overcome this, the study introduces a three-phase approach: MDNR-based NARMA-L2 controller Pre-process and Identification, Enhancement of NARMA-L2 controller-based NARMA(L2)- F O P + D ( A N F I S ) μ - I λ , and Evaluation of FOPID criteria-based AOA. This approach integrates NARMA-L2 for criterion weighting and ANFIS for AOA selection, validated through NARMA(L2)- F O P + D ( A N F I S ) μ - I λ evaluation, showcasing the efficacy of the proposed methodology. |
| Author | Marhoon, Hamzah M. Mohammed, Abdullah Fadhil Basil, Noorulden |
| Author_xml | – sequence: 1 givenname: Hamzah M. surname: Marhoon fullname: Marhoon, Hamzah M. organization: Systems Engineering Department, College of Information Engineering, Al-Nahrain University – sequence: 2 givenname: Noorulden orcidid: 0000-0003-0847-2611 surname: Basil fullname: Basil, Noorulden email: noorulden@uomustansiriyah.edu.iq organization: Department of Electrical Engineering, College of Engineering, Mustansiriyah University – sequence: 3 givenname: Abdullah Fadhil surname: Mohammed fullname: Mohammed, Abdullah Fadhil organization: Department of Electrical Engineering, College of Engineering, Mustansiriyah University |
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| Cites_doi | 10.1016/j.bspc.2021.103135 10.1016/j.ultsonch.2023.106441 10.1007/s41870-023-01508-z 10.1002/btm2.10359 10.1109/TASE.2013.2265135 10.1016/j.matdes.2023.111735 10.1109/TSMCC.2007.905812 10.1016/j.knosys.2023.110722 10.1002/aisy.202200061 10.1515/ntrev-2022-0115 10.1007/s40820-023-01095-5 10.1007/s13204-021-02034-9 10.31272/jeasd.26.2.10 10.1080/1448837X.2024.2308415 10.1016/j.prime.2023.100305 10.1016/j.bmt.2022.11.004 10.1007/978-3-031-28715-2_10 10.1002/aisy.202100279 10.1016/j.techfore.2023.122588 10.1088/1757-899X/928/3/032017 10.1007/978-981-16-6022-1_10 10.1016/j.eswa.2023.120775 10.1155/2020/8870106 10.31763/ijrcs.v2i3.798 10.1016/j.rico.2023.100279 10.1177/09544100211029814 10.1146/annurev-control-061521-070251 10.1016/j.jare.2022.08.015 10.1002/smll.202202962 10.1007/s11569-021-00406-7 10.1007/s40997-023-00596-3 10.1016/j.measen.2022.100640 10.1007/s00204-023-03471-x 10.31272/jeasd.25.1.8 10.1016/j.measen.2023.100672 10.1016/j.glohj.2023.02.008 10.1016/j.biopha.2022.114131 10.1016/j.engappai.2017.04.016 10.1016/j.biopha.2020.111103 10.3390/mi13112028 10.1007/s11042-022-14293-x 10.1007/s41870-023-01571-6 10.1155/2022/5877042 10.1002/aisy.202100036 10.1007/s42452-022-05164-z 10.1016/j.biopha.2023.114784 |
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| Keywords | NARMA-L2 controller Medical Defense Nanorobots ANFIS Fractional order proportional integral derivative controller Archimedes Optimization Algorithm |
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| References | Z Liu (1724_CR41) 2023; 47 N Basil (1724_CR7) 2024 R Mukherjee (1724_CR15) 2021; 5 S Kumar (1724_CR27) 2022; 19 A Kumar (1724_CR51) 2023 NB Mohamadwasel (1724_CR33) 2021; 13 M Abdelaziz (1724_CR6) 2022; 13 L Li (1724_CR26) 2023; 15 AV Singh (1724_CR21) 2023; 97 N Basil (1724_CR24) 2023; 26 W Lu (1724_CR1) 2021; 134 Y Al-Dunainawi (1724_CR16) 2017; 62 H Manoharan (1724_CR2) 2022 JF de Canete (1724_CR45) 2023; 132 J Jiang (1724_CR3) 2022; 4 UAK Betz (1724_CR23) 2023; 193 K Vergidis (1724_CR47) 2007; 38 Y Guo (1724_CR29) 2022; 5 C Yang (1724_CR13) 2022; 48 N Basil (1724_CR9) 2023; 6 YR Mohammed (1724_CR36) 2020; 29 X Ma (1724_CR4) 2020 AV Singh (1724_CR19) 2023; 163 K Komiya (1724_CR38) 2022; 4 AE Altyar (1724_CR20) 2023; 158 AB Moniz (1724_CR30) 2022; 16 A Dehghani-Barenji (1724_CR46) 2022; 71 A Manero (1724_CR18) 2023; 8 M Javaid (1724_CR10) 2023; 1 P Jeyananthan (1724_CR50) 2023 N Basil (1724_CR37) 2022 Y Zhao (1724_CR31) 2022; 4 AR Ibrahim (1724_CR48) 2021; 12 N Basil (1724_CR17) 2023; 12 SM Abed (1724_CR34) 2022; 4 Y Liu (1724_CR12) 2023; 96 NB Mohamadwasel (1724_CR40) 2020; 928 N Basil (1724_CR14) 2023; 276 F Lu (1724_CR43) 2022; 236 H Marino (1724_CR8) 2014; 11 HM Marhoon (1724_CR44) 2021; 12 FS Raheem (1724_CR28) 2023; 25 A Adnan (1724_CR42) 2022; 26 A Haleem (1724_CR25) 2023; 7 H Shen (1724_CR11) 2023; 227 MA Ali (1724_CR49) 2021; 25 P Jain (1724_CR35) 2022; 11 LE Alatabani (1724_CR22) 2023 MS Abed (1724_CR32) 2022 N Basil (1724_CR39) 2022 BEF De Ávila (1724_CR5) 2018; 3 |
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| Title | Medical Defense Nanorobots (MDNRs): a new evaluation and selection of controller criteria for improved disease diagnosis and patient safety using NARMA(L2)-FOP + D(ANFIS)µ – Iλ-based Archimedes Optimization Algorithm |
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